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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.04569 |
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| _version_ | 1866911360230621184 |
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| author | Zhao, Hailiang Wang, Ziqi Hu, Daojiang Ling, Zhiwei Qian, Wenzhuo Zhai, Jiahui Yang, Yuhao Gao, Zhipeng Liu, Mingyi Di, Kai Zhao, Xinkui Wang, Zhongjie Yin, Jianwei Zhou, MengChu Deng, Shuiguang |
| author_facet | Zhao, Hailiang Wang, Ziqi Hu, Daojiang Ling, Zhiwei Qian, Wenzhuo Zhai, Jiahui Yang, Yuhao Gao, Zhipeng Liu, Mingyi Di, Kai Zhao, Xinkui Wang, Zhongjie Yin, Jianwei Zhou, MengChu Deng, Shuiguang |
| contents | The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04569 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0 Zhao, Hailiang Wang, Ziqi Hu, Daojiang Ling, Zhiwei Qian, Wenzhuo Zhai, Jiahui Yang, Yuhao Gao, Zhipeng Liu, Mingyi Di, Kai Zhao, Xinkui Wang, Zhongjie Yin, Jianwei Zhou, MengChu Deng, Shuiguang Computational Engineering, Finance, and Science The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments. |
| title | Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0 |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2601.04569 |